The notes we discussed in class the last day are here, 20190502 Simple Linear Regression Notes

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The notes we discussed in class the last day are here, 20190502 Simple Linear Regression Notes
Here is the summary page with the basic equations for Inferences on the parameters known as the population mean and population proportion. Summary Lessons 13, 14, 15, 16 Inferences on Mean and Proportion
The summary page developed in class for Lesson 14 is here. OK oil well problem Z test.
Another oil well example with 3 cases and more detail is here, Class Notes . These cases have different numbers that the cases considered in class, but are the same problems. The cases demonstrate the difference between strong evidence, no evidence, and moderate evidence against the null hypothesis and in favor of the alternative.
See Poisson Random Variable Lesson 9 Hand Written Lecture for some lecture ideas and details on the class example.
STAT 2023 on Tuesday, February 19, 2019, will have class online due to inclement weather. Begin to learn the material by reading Lesson 8 and studying in detail Binomial Random Variable Lesson 8 Written Out Lecture Once you complete the studying and read the paragraphs below, use this exercise, 20190219 STAT 2023 Exercise Lesson 8, to practice the ideas. Remember the key to the exercise is posted.
An online statistical calculator that can be used to calculate probabilities associated with various distributions is at https://stattrek.com/ Look on the left side of the main page for Binomial under Stat Tables. To use the online Binomial calculator plug in the value of p in the top box, the value of n below that, and the value of the variable for which the probability is being calculated.
Many of your calculators can calculate the Binomial Probabilities. Check YouTube for videos about how to use your calculator to generate the Binomial probabilities. On TI calculators try the following. 2nd (screen) =>VARS=>binomial pdf (n,p,x) which is the Binomial probability at the value of x with n as the number of independent trials and p as probability of success on one outcome. In notation, that is P(X=x X~Bi(n,p)). If you used binomial cdf(n,p,x) then you have the cumulative probability on each value of the Binomial variable from 0 to the value of x. In notation, that would be P(X<=x X~Bi(n,p)).